OpenNLP NER Extraction Index Stage

Named Entity Recognition (NER) is the task of finding the names of persons, organizations, locations, and/or things in a passage of free text. The OpenNLP NER Extraction index stage (previously called the OpenNLP NER Extractor stage) uses a set of rules to find named entities in a field in the Pipeline Document (the "source") and populates a new fields (the "target") with these entities.

The Name Finder tool can detect named entities and numbers in text. To be able to detect entities the Name Finder needs a model. The model is dependent on the language and entity type it was trained for. The OpenNLP projects offers a number of pre-trained name finder models which are trained on various freely available corpora. They can be downloaded at our model download page. To find names in raw text the text must be segmented into tokens and sentences.

See this video tutorial for a demonstration of how to configure this stage:

The Fusion directory fusion/data/nlp contains a set of NER models for English, as well as sentence, token, and part-of-speech models.

Before they can be used, model files must be uploaded to Fusion using the Fusion Blob Store service via the REST API. Here is an example of how to upload the sentence model file from the fusion using the curl command-line utility, where admin is the name of a user with admin privileges, and pass is the password: